Autonomous driving paper index
A Convolutional Neural Network Method for Self-Driving Cars*
One-line summary
The profound impacts of driverless vehicles technology could change to our society remarkably, not to mention the significant enhancements they could bring to the overall safety, efficiency, and convenience of transportation and transit systems.
Engineering notes
Key topics: self-driving car, self-driving, autonomous vehicle. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
The profound impacts of driverless vehicles technology could change to our society remarkably, not to mention the significant enhancements they could bring to the overall safety, efficiency, and convenience of transportation and transit systems. This paper employs deep neural networks to predict the steering angle and throttle values for an autonomous vehicle by obtained images taken from different viewpoints. The proposed convolutional neural network is able to extract the features from the images and find the dependencies for forecasting the steering angle and the speed to keep the vehicle running at the center of the lane automatically. The synthetic images used in our work is generated from Udacity platform.
Links and sources
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